Which integrations are available for this server?

Enables querying and analyzing SQLite databases, including executing SQL queries, listing tables and schemas, and performing data analysis operations on database contents.

How do I use MCP Tabular Data Analysis Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Tabular Data Analysis Server show me the top 5 sales categories from sample_sales.csv" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Tabular Data Analysis Server

A Model Context Protocol (MCP) server that provides tools for analyzing numeric and tabular data. Works with CSV files and SQLite databases.

Demo

auto_insights

data_quality_report

analyze_time_series

Features

Core Tools

Tool	Description
`list_data_files`	List available CSV and SQLite files in the data directory
`describe_dataset`	Generate statistics for a dataset (shape, types, distributions, missing values)
`detect_anomalies`	Find outliers using Z-score or IQR methods
`compute_correlation`	Calculate correlation matrices between numeric columns
`filter_rows`	Filter data using various operators (eq, gt, lt, contains, etc.)
`group_aggregate`	Group data and compute aggregations (sum, mean, count, etc.)
`query_sqlite`	Execute SQL queries on SQLite databases
`list_tables`	List all tables and schemas in a SQLite database

Analytics Tools

Tool	Description
`create_pivot_table`	Create Excel-style pivot tables with flexible aggregations
`data_quality_report`	Data quality assessment with scores and recommendations
`analyze_time_series`	Time series analysis with trends, seasonality, and moving averages
`generate_chart`	Create visualizations (bar, line, scatter, histogram, pie, box plots)
`merge_datasets`	Join/merge two datasets together (inner, left, right, outer joins)
`statistical_test`	Hypothesis testing (t-test, ANOVA, chi-squared, correlation tests)
`auto_insights`	Discover patterns and insights
`export_data`	Export filtered/transformed data to new CSV files

Installation

Prerequisites

Python 3.10+
uv (recommended) or pip

Install with uv

cd mcp-tabular uv sync

Install with pip

cd mcp-tabular pip install -e .

Usage

Running the Server Directly

# With uv uv run mcp-tabular # With pip installation mcp-tabular

Configure with Claude Desktop

Locate your Claude Desktop config file:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Windows: %APPDATA%\Claude\claude_desktop_config.json
- Linux: ~/.config/Claude/claude_desktop_config.json
Add this configuration (replace /Users/kirondeb/mcp-tabular with your actual path):

{ "mcpServers": { "tabular-data": { "command": "/Users/kirondeb/mcp-tabular/.venv/bin/python", "args": [ "-m", "mcp_tabular.server" ] } } }

Restart Claude Desktop (quit and reopen)
Test by asking Claude: "Describe the dataset in data/sample_sales.csv"

See CONNECT_TO_CLAUDE_DESKTOP.md for detailed instructions and troubleshooting.

See TEST_PROMPTS.md for example prompts.

Sample Data

The project includes sample data for testing:

data/sample_sales.csv - Sales transaction data
data/sample.db - SQLite database with customers, orders, and products tables

To create the SQLite sample database:

python scripts/create_sample_db.py

Path Resolution

All file paths are resolved relative to the project root directory:

Relative paths like data/sample_sales.csv work from any working directory
Absolute paths also work as expected
Paths resolve relative to where mcp_tabular is installed

Tool Examples

List Data Files

List available data files:

list_data_files()

Lists all CSV and SQLite files in the data directory with metadata.

Describe Dataset

Generate statistics for a dataset:

describe_dataset(file_path="data/sample_sales.csv")

Includes shape, column types, numeric statistics (mean, std, median, skew, kurtosis), categorical value counts, and a sample preview.

Detect Anomalies

Find outliers in numeric columns:

detect_anomalies( file_path="data/sample_sales.csv", column="total_sales", method="zscore", threshold=3.0 )

Supports zscore and iqr methods.

Compute Correlation

Calculate correlations between numeric columns:

compute_correlation( file_path="data/sample_sales.csv", method="pearson" )

Includes full correlation matrix and top correlations ranked by strength.

Filter Rows

Filter data based on conditions:

filter_rows( file_path="data/sample_sales.csv", column="category", operator="eq", value="Electronics" )

Operators: eq, ne, gt, gte, lt, lte, contains, startswith, endswith

Group & Aggregate

Group data and compute aggregations:

group_aggregate( file_path="data/sample_sales.csv", group_by=["category", "region"], aggregations={"total_sales": ["sum", "mean"], "quantity": ["count"]} )

Query SQLite

Execute SQL queries on databases:

query_sqlite( db_path="data/sample.db", query="SELECT * FROM customers WHERE lifetime_value > 1000" )

List Tables

List tables and schemas in a SQLite database:

list_tables(db_path="data/sample.db")

Advanced Analytics Examples

Create Pivot Table

Create Excel-style pivot tables:

create_pivot_table( file_path="data/sample_sales.csv", index=["region"], columns=["category"], values="total_sales", aggfunc="sum" )

Data Quality Report

Generate a data quality assessment:

data_quality_report(file_path="data/sample_sales.csv")

Includes completeness score, duplicate detection, outlier analysis, and an overall quality grade (A-F).

Time Series Analysis

Analyze trends and seasonality:

analyze_time_series( file_path="data/sample_sales.csv", date_column="order_date", value_column="total_sales", freq="M", include_forecast=True )

Generate Charts

Create visualizations (returned as base64 images):

generate_chart( file_path="data/sample_sales.csv", chart_type="bar", x_column="category", y_column="total_sales", title="Sales by Category" )

Supported chart types: bar, line, scatter, histogram, pie, box

Merge Datasets

Join or merge two datasets:

merge_datasets( file_path_left="data/orders.csv", file_path_right="data/customers.csv", on=["customer_id"], how="left" )

Statistical Testing

Run hypothesis tests:

statistical_test( file_path="data/sample_sales.csv", test_type="ttest_ind", column1="total_sales", group_column="region", alpha=0.05 )

Supported tests: ttest_ind, ttest_paired, chi_squared, anova, mann_whitney, pearson, spearman

Auto Insights

Discover patterns and insights:

auto_insights(file_path="data/sample_sales.csv")

Includes insights about correlations, outliers, skewed distributions, missing data, and more.

Export Data

Export filtered data to a new CSV:

export_data( file_path="data/sample_sales.csv", output_name="electronics_sales", filter_column="category", filter_operator="eq", filter_value="Electronics", sort_by="total_sales", sort_ascending=False )

Development

Run Tests

uv run pytest

Project Structure

mcp-tabular/ ├── src/ │ └── mcp_tabular/ │ ├── __init__.py │ └── server.py # Main MCP server implementation ├── data/ │ ├── sample_sales.csv # Sample CSV data │ └── sample.db # Sample SQLite database ├── scripts/ │ └── create_sample_db.py ├── pyproject.toml ├── claude_desktop_config.json └── README.md

License

MIT